Dataset Information

Pathway-based personalized analysis of breast cancer expression data.

ABSTRACT: Most analyses of high throughput cancer data represent tumors by "atomistic" single-gene properties. Pathifier, a recently introduced method, characterizes a tumor in terms of "coarse grained" pathway-based variables.We applied Pathifier to study a very large dataset of 2000 breast cancer samples and 144 normal tissues. Pathifier uses known gene assignments to pathways and biological processes to calculate for each pathway and tumor a Pathway Deregulation Score (PDS). Individual samples are represented in terms of their PDSs calculated for several hundred pathways, and the samples of the data set are analyzed and stratified on the basis of their profiles over these "coarse grained", biologically meaningful variables.We identified nine tumor subtypes; a new subclass (comprising about 7% of the samples) exhibits high deregulation in 38 PKA pathways, induced by overexpression of the gene PRKACB. Another interesting finding is that basal tumors break into two subclasses, with low and high deregulation of a cluster of immune system pathways. High deregulation corresponds to higher concentrations of Tumor Infiltrating Lymphocytes, and the patients of this basal subtype have better prognosis. The analysis used 1000 "discovery set" tumors; our results were highly reproducible on 1000 independent "validation" samples.The coarse-grained variables that represent pathway deregulation provide a basis for relevant, novel and robust findings for breast cancer. Our analysis indicates that in breast cancer reliable prognostic signatures are most likely to be obtained by treating separately different subgroups of the patients.

SUBMITTER: Livshits A

PROVIDER: S-EPMC5528809 | biostudies-other | 2015 Aug

REPOSITORIES: biostudies-other

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Pathway-based personalized analysis of breast cancer expression data.

Livshits Anna A Git Anna A Fuks Garold G Caldas Carlos C Domany Eytan E

Molecular oncology 20150429 7

<h4>Introduction</h4>Most analyses of high throughput cancer data represent tumors by "atomistic" single-gene properties. Pathifier, a recently introduced method, characterizes a tumor in terms of "coarse grained" pathway-based variables.<h4>Methods</h4>We applied Pathifier to study a very large dataset of 2000 breast cancer samples and 144 normal tissues. Pathifier uses known gene assignments to pathways and biological processes to calculate for each pathway and tumor a Pathway Deregulation Sco ...[more]

PMID: 25963740

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Project description:Breast cancer is a highly heterogeneous disease that is clinically classified into several subtypes. Among these subtypes, basal-like breast cancer largely overlaps with triple-negative breast cancer (TNBC), and these two groups are generally studied together as a single entity. Differences in the molecular makeup of breast cancers can result in different treatment strategies and prognoses for patients with different breast cancer subtypes. Compared with other subtypes, basal-like and other ER+ breast cancer subtypes exhibit marked differences in etiologic factors, clinical characteristics and therapeutic potential. Anthracycline drugs are typically used as the first-line clinical treatment for basal-like breast cancer subtypes. However, certain patients develop drug resistance following chemotherapy, which can lead to disease relapse and death. Even among patients with basal-like breast cancer, there can be significant molecular differences, and it is difficult to identify specific drug resistance proteins in any given patient using conventional variance testing methods. Therefore, we designed a new method for identifying drug resistance genes. Subgroups, personalized biomarkers, and therapy targets were identified using cluster analysis of differentially expressed genes. We found that basal-like breast cancer could be further divided into at least four distinct subgroups, including two groups at risk for drug resistance and two groups characterized by sensitivity to pharmacotherapy. Based on functional differences among these subgroups, we identified nine biomarkers related to drug resistance: SYK, LCK, GAB2, PAWR, PPARG, MDFI, ZAP70, CIITA and ACTA1. Finally, based on the deviation scores of the examined pathways, 16 pathways were shown to exhibit varying degrees of abnormality in the various subgroups, indicating that patients with different subtypes of basal-like breast cancer can be characterized by differences in the functional status of these pathways. Therefore, these nine differentially expressed genes and their associated functional pathways should provide the basis for novel personalized clinical treatments of basal-like breast cancer.

Project description:BACKGROUND:As protein is the basic unit of cell function and biological pathway, shotgun proteomics, the large-scale analysis of proteins, is contributing greatly to our understanding of disease mechanisms. Proteomics study could detect the changes of both protein expression and modification. With the releases of large-scale cancer proteome studies, how to integrate acquired proteomic and phosphoproteomic data in more comprehensive pathway analysis becomes implemented, but remains challenging. Integrative pathway analysis at proteome level provides a systematic insight into the signaling network adaptations in the development of cancer. RESULTS:Here we integrated proteomic and phosphoproteomic data to perform pathway prioritization in breast cancer. We manually collected and curated breast cancer well-known related pathways from the literature as target pathways (TPs) or positive control in method evaluation. Three different strategies including Hypergeometric test based over-representation analysis, Kolmogorov-Smirnov (K-S) test based gene set analysis and topology-based pathway analysis, were applied and evaluated in integrating protein expression and phosphorylation. In comparison, we also assessed the ranking performance of the strategy using information of protein expression or protein phosphorylation individually. Target pathways were ranked more top with the data integration than using the information from proteomic or phosphoproteomic data individually. In the comparisons of pathway analysis strategies, topology-based method outperformed than the others. The subtypes of breast cancer, which consist of Luminal A, Luminal B, Basal and HER2-enriched, vary greatly in prognosis and require distinct treatment. Therefore we applied topology-based pathway analysis with integrating protein expression and phosphorylation profiles on four subtypes of breast cancer. The results showed that TPs were enriched in all subtypes but their ranks were significantly different among the subtypes. For instance, p53 pathway ranked top in the Basal-like breast cancer subtype, but not in HER2-enriched type. The rank of Focal adhesion pathway was more top in HER2- subtypes than in HER2+ subtypes. The results were consistent with some previous researches. CONCLUSIONS:The results demonstrate that the network topology-based method is more powerful by integrating proteomic and phosphoproteomic in pathway analysis of proteomics study. This integrative strategy can also be used to rank the specific pathways for the disease subtypes.

Project description:The potential of applying data analysis tools to microarray data for diagnosis and prognosis is illustrated on the recent breast cancer dataset of van 't Veer and coworkers. We re-examine that dataset using the novel technique of logical analysis of data (LAD), with the double objective of discovering patterns characteristic for cases with good or poor outcome, using them for accurate and justifiable predictions; and deriving novel information about the role of genes, the existence of special classes of cases, and other factors.Data were analyzed using the combinatorics and optimization-based method of LAD, recently shown to provide highly accurate diagnostic and prognostic systems in cardiology, cancer proteomics, hematology, pulmonology, and other disciplines.LAD identified a subset of 17 of the 25,000 genes, capable of fully distinguishing between patients with poor, respectively good prognoses. An extensive list of 'patterns' or 'combinatorial biomarkers' (that is, combinations of genes and limitations on their expression levels) was generated, and 40 patterns were used to create a prognostic system, shown to have 100% and 92.9% weighted accuracy on the training and test sets, respectively. The prognostic system uses fewer genes than other methods, and has similar or better accuracy than those reported in other studies. Out of the 17 genes identified by LAD, three (respectively, five) were shown to play a significant role in determining poor (respectively, good) prognosis. Two new classes of patients (described by similar sets of covering patterns, gene expression ranges, and clinical features) were discovered. As a by-product of the study, it is shown that the training and the test sets of van 't Veer have differing characteristics.The study shows that LAD provides an accurate and fully explanatory prognostic system for breast cancer using genomic data (that is, a system that, in addition to predicting good or poor prognosis, provides an individualized explanation of the reasons for that prognosis for each patient). Moreover, the LAD model provides valuable insights into the roles of individual and combinatorial biomarkers, allows the discovery of new classes of patients, and generates a vast library of biomedical research hypotheses.

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Pathway-based personalized analysis of breast cancer expression data.

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Pathway-based personalized analysis of breast cancer expression data.

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